From our lens, certain technology vendors and bloggers have been confusing manufacturers and supply chain teams by communicating alarming messages on the growth of so-termed, “big-data” supply chains. Some messages relate to organizations drowning in data while others offer a panacea of remedies to get data under control.
Supply Chain Matters recommends that our readers take the opportunity to review a recently published McKinsey Quarterly article, Are you ready for the era of ‘big-data’? This advisory article is insightful and well written and provides a proactive context as to how organizations can leverage the harvesting of data for competitive advantage. While McKinsey acknowledges that these are still early days for big data, the authors state that their research indicates that organizations that leverage data and business analytics to guide decision making can gain an edge in strategically engaging customers and suppliers. We would add that the recent major supply chain disruptions precipitated by the tragedies in northern Japan and now Thailand, have also provided real-time reminders on the importance of having the right information.
The article also makes a very important conclusion. Some industries will realize benefits sooner, namely because they have strong incentives to do so, along with their overall readiness to capitalize on data management strategies.
The report features five well posed questions that senior executives should be asking themselves.
One question reflects on breaking down the barriers of accessibility to industry and supply chain wide data when organizations feel threatened or gain strategic benefit from sharing their perceived proprietary or confidential data. These barriers exist externally as well as internally. As an example, some big retailers continue to harvest financial gains from selling point-of-sales data. Industry data aggregators or intermediaries benefit by selling such data to industry suppliers. The good news however as that supply chain organizations are finding innovative and collaborative means to gain broader access to data. At a recent industry conference, we heard one presenter representing a consumer goods company state that a revolution is underway in opening up access to direct sales data, even among industry competitors.
Another insightful question posed was the following: “If you could test all of your decisions, how would that change the way you compete?” The notion here is that access and leverage of key information and insights facilitates a fundamentally different type of decision- making process, one based on reducing the variability of outcomes by testing or predicting possible decision scenarios ahead of time. A supply chain specific example of this analytical or predictive decision making context has been the popular adoption of multi-echelon inventory optimization technology. This software leverages key data related to supply chain footprint, products, inventory cost, transportation and cycle time to analyze various inventory tradeoffs and deployment strategies that can achieve a specific customer service level. Thus, a decision related to servicing a key customer, can be analyzed ahead of time for quantification of impacts in overall costs and service levels. Other and more recent examples are the Kinaxis and Progress Software announcements of the availability supply chain control tower applications that leverage either scenario management or business process management outcome features.
Other questions posed relate to how the business would change, if big data were channeled, how these methods would augment management decision-making, and the opportunities for certain companies to create entirely new information-driven business models .
A final McKinsey observation concerns a pending shortage of skilled resources, one that this author has communicated in prior talks on predictive analytics. McKinsey research quantifies that in the U.S. alone, “demand for skilled analytical people can outstrip supply by 50 to 60 percent.” McKinsey notes: ”By 2018, as many as 140,000 to 190,000 additional specialists many be required.” This message is rather important to dwell upon. Supply chain professionals at all levels need to proactively upgrade their skills to include these new areas of leveraging analytical data and predictive decision-making. Companies need to provide more incentives and opportunities for training in these areas, and universities and training organizations need to broaden their curriculum to embrace advanced analytical and predictive decision-making methods.
A final note reflects on the current landscape of supply chain focused professional certification programs, such as those offered by APICS, CSCMP and ISM. By our point-of-view, there needs to be more exam content devoted to candidate understanding of these evolving data-driven and predictive decision-making processes vs. those that sufficed in the prior times of sequential based planning such as MPS and MRP.
The times are changing along with the potential and means for more predictive supply chain decision-making. And so are the skills and readiness qualifications.
Bob Ferrari
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